NanoGram Detection of Drilling Fluids Additives for Uncertainty Reduction in Surface Logging

Manual sampling rock cuttings off the shale shaker for lithology and petrophysical characterization is frequently performed during mud logging. Knowing the depth origin where the cuttings were generated is very important for correlating the cuttings to the petrophysical characterization of the formation. It is a challenge to accurately determine the depth origin of the cuttings, especially in horizontal sections and in coiled tubing drilling, where conventional logging while drilling is not accessible. Additionally, even in less challenging drilling conditions, many factors can contribute to an inaccurate assessment of the depth origin of the cuttings. Inaccuracies can be caused by variation of the annulus dimension used to determine the lag time (and thus the depth of the cuttings), by the shifting or scrambling of cuttings during their return trip back to the surface, and by the mislabelling of the cuttings during sampling. In this work, we report the synthesis and application of polystyrenic nanoparticles (NanoTags) in labeling cuttings for depth origin assessment. We have successfully tagged cuttings using two NanoTags during a drilling field test in a carbonate gas well and demonstrated nanogram detection capability of the tags via pyrolysis-GCMS using an internally developed workflow. The cuttings depth determined using our tags correlates well with the depth calculated by conventional mud logging techniques.

A Novel and Sustainable Generation of Advanced Mud Gas Logging System for Managed Pressure Drilling Applications: An Explorative Well Deployment from North Germany

Summary In the high risk Managed Pressure Drilling operations, increased certainty given by Mud Logging is a critical deliverable to guarantee a safe drilling environment even under challenging conditions and, to provide the first indications for reservoir evaluation. This paper describes a novel product application that successfully obtains advanced mud gas data from a Managed Pressure Drilling environment, proven in flow-loop and field applications (in Lower Saxony, Germany), by reducing service footprint as well as power consumption.

New Advances in Surface Data Logging Technologies for Comprehensive Real-Time Petrophysical Evaluation to Optimize Logging Programs in a Mature Field; A Case History, Onshore, Abu Dhabi

The challenges of drilling new wells are increasingly associated with minimizing HSE risks, that relate to chemical radioactive sources in the Bottom Hole Assembly for formation evaluation. Drilling risks such as differential sticking, also necessitates investigation of alternative petrophysical data gathering methodologies that can fulfil these requirements. Surface Data Logging presents a viable alternative in mature fields, satisfying petrophysical data gathering and interpretation in real-time as well, as traditional geological applications and offset well correlations in a way, to optimize well construction costs. During the planning phase, a fully integrated approach was adopted including advanced cutting and advanced gas analysis to be deployed, in this case study, well together with experienced well site personnel. A comprehensive pre-well study was conducted reviewing all offset nearby wells data. The workflow included provision of full real-time advanced cuttings and gas analysis for formation evaluation and reservoir fluid composition, lithology description, and addressing effective hole cleaning concerns. The advanced Mud Logging services was run in parallel to the Logging While Drilling services for a few pilot wells, in order to correlate downhole tool parameters, with respect to data quality control, to identify the petrophysical character of the formation markers for benchmarking future data gathering requirements. In addition to the potential use of standalone fully integrated advanced Mud Logging to reduce risks and minimize field development costs. With the help of experienced wellsite geologist on location and real time advanced gas detection utilizing high resolution mass spectrometer and X-Ray fluorescence (XRF) and X-Ray Diffraction (XRD) data, geological boundaries and formations tops were accurately identified across the whole drilled interval. Modern and advanced interpretation techniques for the integrated analysis were proven to be effective in determining sweet spots of the reservoir, fluid type, and overall reservoir quality. Deployment of fully integrated mud logging solutions with new interpretation methodologies can be effective in providing a better understanding of reservoir geological and petrophysical characteristics in real-time, offering viable alternative for minimizing formation evaluation sensors in the BHA, particularly eliminating radioactive sources, while reducing overall developments costs, without sacrificing formation evaluation requirements.

NanoTags for Improved Cuttings Depth Correlation

During a drilling operation, rock cuttings are often sampled off a shale shaker for lithology and petrophysical characterization. These analyses play an important role in describing the subsurface, and it is important that the depth origin of the cuttings be accurately determined. Traditionally, mud loggers determine the depth origin of the sampled cuttings by calculating the lag time required for the cuttings to travel from the bit to the surface. These calculations, however, can contain inaccuracies in the depth correlation due to the shuffling and settling of cuttings as they travel with drilling fluid to the surface, due to unplanned conditions like drilling an overgauge hole, and due to other unforeseen drilling events, especially critical in horizontal sections. We, therefore, aimed to remedy these inaccuracies by developing a series of styrene-based nanoparticles that tagged the cuttings as they were generated at the drill bit. These “NanoTags” were tested while drilling in Q4 2019, and the results indicated that the NanoTags did, in fact, have the potential to identify some systematic errors compared with traditional mud-logging calculations.

New Method for Predicting Casing Wear in Highly Deviated Wells Using Mud Logging Data

Casing wear is an essential and complex phenomenon in oil and gas wells. Research is being conducted to predict this phenomenon. This study was conducted at a well in southwestern Iran. In this paper, first examine the force exerted on the drill string. Next, the contact force between the drill string and the casing is calculated. Finally, the wear volume and the depth of the wear groove are determined. These calculations were performed using MATLAB and Python software. In addition, due to the high accuracy of coding, mud log data was used to make the results more accurate. It has also been shown that increasing RPM increases the depth of wear and attempts to drill a highly deviated wells as a sliding mode. Finally, compared the results and matched them with the wireline logs recorded from the well.

Application of Machine Learning for Lithology-on-Bit Prediction using Drilling Data in Real-Time

SUMMARY Researchers base their analysis on basic drilling parameters obtained during mud logging and demonstrate impressive results. However, due to limitations imposed by data quality often present during drilling, those solutions often tend to lose their stability and high levels of predictivity. In this work, the concept of hybrid modeling was introduced which allows to integrate the analytical correlations with algorithms of machine learning for obtaining stable solutions consistent from one data set to another.

Well Logging Analysis in Niger Delta Basin

A hydrocarbon exploration begins with the geological recognition of probable hydrocarbon accumulation areas, which are confirmed by seismic survey and, to ensure certainty, it is necessary to drill a well. On those exploration wells measurements are made down the hole for the formation evaluation. The evaluation of water, oil and gas saturations are attained by geological and petrophysical characteristics. To obtain such information it is necessary to use a combination of several sources, namely mud logging, coring, well logging and occasionally down the hole tests. Due to the high risks associated with drilling activities, such as safety problems and environmental impacts, it is extremely important to have a very well designed and established drilling program. Therefore, the activities monitoring and control and a good knowledge of what types of formations will be affected, and its principal characteristics, are priorities to take into consideration. Bearing in mind the sources for the formation evaluation, the present work aims to focus on wireline logs and the major challenge that is faced here which is data acquisition and petrophysical evaluation. The case study is a Niger Delta basin (Nigeria).

NANOTAGS FOR IMPROVED CUTTING DEPTH CORRELATION

During a drilling operation, rock cuttings are often sampled off a shale shaker for lithology and petrophysical characterization. These analyses play an important role in describing the subsurface; and it is important that the depth origin of the cuttings be accurately determined. Traditionally, mud-loggers determine the depth origin of the sampled cuttings by calculating the lag time required for the cuttings to travel from the bit to the surface. These calculations, however, can contain inaccuracies in the depth correlation due to the shuffling and settling of cuttings as they travel with drilling fluid to the surface, due to unplanned conditions like drilling an overgauge hole, and due to other unforeseen drilling events, especially critical in horizontal sections. We therefore aimed to remedy these inaccuracies by developing a series of styrene-based nanoparticles that tagged the cuttings as they were generated at the drillbit. These “NanoTags” were tested while drilling in Q4, 2019; and the results indicated that the NanoTags did in fact have the potential to identify some systematic errors compared with traditional mud logging calculations.

Machine Learning for Deepwater Drilling: Gas-Kick-Alarm Classification Using Pilot-Scale Rig Data with Combined Surface-Riser-Downhole Monitoring

Summary Gas kicks occur frequently in deepwater drilling because of the extremely narrow mud-weight window [minimum 0.01 specific gravity (sg)]. The traditional kick-detection method mainly relies on the driller's analysis of monitored compound comprehensive mud-logging data. However, the traditional method has significant time lag, including missed and false detection, and often leads to severe gas influxes during deepwater drilling. A novel machine-learning (ML) model is presented here using pilot-scale rig data combined with surface-riser-downhole monitoring for gas-kick early detection and risk classification. A series of pilot-scaletest-well experiments (a total of 108 tests) are performed to simulate deepwater gas kicks and produce a multisource data set through fusion of comprehensive mud-logging data from surface monitoring, acoustic data from riser-monitoring technologies, and measurement-while-drilling data [e.g., bottomhole pressure (BHP)] from downhole monitoring technologies. During these experiments, the deepwater blowout preventer (BOP) is simulated using a variable cross section of crossover (X/O; equipped with booster-flow pipes); the Coriolis flowmeter is installed in the mud-return pipe to accurately measure flow out; the acoustic wave sensors are installed outside of the riser section (X/O) to monitor gas migration; and the downhole memory pressure gauges are installed to monitor BHP. Next, data preparation and data analysis are performed including raw-data exploration, data cleaning, signal/noise-ratio (SNR) analysis, feature scaling, outlier detection, and feature engineering. Further, a novel and improved data-labeling criterion for gas-kick alarms is proposed, with six levels (displayed using different colors) instead of two-state alarms (“kick” or “no kick”). The proposed gas-kick-alarm classification is in accordance with the actual field practices. Subsequently, four ML algorithms—decision tree (DT), k-nearest neighbors (KNN), support vector machine (SVM), and long short-term memory (LSTM)—are developed through the complete workflow, beginning with the data allocation and followed by building, evaluation, and optimization of each ML model. Because the LSTM recurrent neural network (RNN) algorithm showed the best performance, it is selected and deployed to early detect gas kicks and classify the corresponding kick alarms. The recall for gas-kick levels corresponding to Risk 0, Risk 1, Risk 2, Risk 3, Risk 4, and Risk 5 are 0.92, 0.93, 0.91, 0.91, 0.92 and 0.92, respectively. Because recall for each gas-kick-alarm level is greater than 0.9, it ensures rare false negatives (FNs) during kick detection. The accuracy, precision, recall, and f1 score of the deployed LSTM model in the testing data set is 91.6%, 0.93, 0.92 and 0.92, respectively. Further, the detection time delay is approximately 2 to 7 seconds only, which provides an improved time margin to take appropriate safety measures, promptly deal with a gas kick through a well-control program, and prevent a potential blowout during deepwater drilling.